Esempi

The basic process is to define parameters, supply training data to generate a
model on, then make predictions based on the model. There are a default set
of parameters that should get some results with most any input, so we'll start
by looking at the data.

Data is supplied in either a file, a stream, or as an array. If supplied in
a file or a stream, it must contain one line per training example, which must
be formatted as an integer class (usually 1 and -1) followed by a series of
feature/value pairs, in increasing feature order. The features are integers,
the values floats, usually scaled 0-1. For example:

-1 1:0.43 3:0.12 9284:0.2

In a document classification problem, say a spam checker, each line would
represent a document. There would be two classes, -1 for spam, 1 for ham.
Each feature would represent some word, and the value would represent that
importance of that word to the document (perhaps the frequency count, with
the total scaled to unit length). Features that were 0 (e.g. the word did
not appear in the document at all) would simply not be included.

In array mode, the data must be passed as an array of arrays. Each sub-array
must have the class as the first element, then key => value sets for the
feature values pairs.

This data is passed to the SVM class's train function, which will return an
SVM model is successful.

Once a model has been generated, it can be used to make predictions about
previously unseen data. This can be passed as an array to the model's
predict function, in the same format as before, but without the label.
The response will be the class.

Models can be saved and restored as required, using the save and load
functions, which both take a file location.